Deep learning-based multimodal fusion of the surface ECG and clinical features in prediction of atrial fibrillation recurrence following catheter ablation.

Journal: BMC medical informatics and decision making
PMID:

Abstract

BACKGROUND: Despite improvement in treatment strategies for atrial fibrillation (AF), a significant proportion of patients still experience recurrence after ablation. This study aims to propose a novel algorithm based on Transformer using surface electrocardiogram (ECG) signals and clinical features can predict AF recurrence.

Authors

  • Yue Qiu
    Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
  • Hongcheng Guo
    State Key Lab of Software Development Environment, Beihang University, Beijing, 100191, China.
  • Shixin Wang
    Institute of Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, China.
  • Shu Yang
    Department of Health Management, Bengbu Medical College, Bengbu, 233030.
  • Xiafeng Peng
    Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China.
  • Dongqin Xiayao
    Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China.
  • Renjie Chen
  • Jian Yang
    Drug Discovery and Development Research Group, College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, SK, Canada.
  • Jiaheng Liu
    State Key Lab of Software Development Environment, Beihang University, Beijing, 100191, China.
  • Mingfang Li
    Xi'an Technological University, Xi'an, China.
  • Zhoujun Li
  • Hongwu Chen
    Division of Cardiology The First Affiliated Hospital of Nanjing Medical University Nanjing China.
  • Minglong Chen
    Division of Cardiology The First Affiliated Hospital of Nanjing Medical University Nanjing China.